Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020)
0.00
0.0017539648 = product of:
0.010523789 = sum of:
0.010523789 = weight(_text_:in in 657) [ClassicSimilarity], result of:
0.010523789 = score(doc=657,freq=4.0), product of:
0.061893053 = queryWeight, product of:
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.045501083 = queryNorm
0.17003182 = fieldWeight in 657, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.0625 = fieldNorm(doc=657)
0.16666667 = coord(1/6)
- Abstract
- The UCLA Library reorganized its software developers into focused subteams with one, the Labs Team, dedicated to conducting experiments. In this article we describe our first attempt at conducting a software development experiment, in which we attempted to improve our digital library's search results with metadata from cloud-based image tagging services. We explore the findings and discuss the lessons learned from our first attempt at running an experiment.